Overview

Dataset statistics

Number of variables10
Number of observations20433
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory113.9 B

Variable types

Numeric9
Categorical1

Alerts

longitude is highly overall correlated with latitudeHigh correlation
latitude is highly overall correlated with longitudeHigh correlation
total_rooms is highly overall correlated with total_bedrooms and 2 other fieldsHigh correlation
total_bedrooms is highly overall correlated with total_rooms and 2 other fieldsHigh correlation
population is highly overall correlated with total_rooms and 2 other fieldsHigh correlation
households is highly overall correlated with total_rooms and 2 other fieldsHigh correlation
median_income is highly overall correlated with targetHigh correlation
target is highly overall correlated with median_incomeHigh correlation

Reproduction

Analysis started2023-02-02 17:24:58.838442
Analysis finished2023-02-02 17:25:09.122602
Duration10.28 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

longitude
Real number (ℝ)

Distinct844
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-119.57069
Minimum-124.35
Maximum-114.31
Zeros0
Zeros (%)0.0%
Negative20433
Negative (%)100.0%
Memory size835.3 KiB
2023-02-02T18:25:09.314053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-124.35
5-th percentile-122.47
Q1-121.8
median-118.49
Q3-118.01
95-th percentile-117.08
Maximum-114.31
Range10.04
Interquartile range (IQR)3.79

Descriptive statistics

Standard deviation2.0035779
Coefficient of variation (CV)-0.01675643
Kurtosis-1.3325482
Mean-119.57069
Median Absolute Deviation (MAD)1.29
Skewness-0.2961409
Sum-2443187.9
Variance4.0143244
MonotonicityNot monotonic
2023-02-02T18:25:09.434843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-118.31 159
 
0.8%
-118.3 157
 
0.8%
-118.29 146
 
0.7%
-118.27 141
 
0.7%
-118.32 141
 
0.7%
-118.28 139
 
0.7%
-118.35 138
 
0.7%
-118.36 135
 
0.7%
-118.19 134
 
0.7%
-118.25 126
 
0.6%
Other values (834) 19017
93.1%
ValueCountFrequency (%)
-124.35 1
 
< 0.1%
-124.3 2
 
< 0.1%
-124.27 1
 
< 0.1%
-124.26 1
 
< 0.1%
-124.25 1
 
< 0.1%
-124.23 3
< 0.1%
-124.22 1
 
< 0.1%
-124.21 3
< 0.1%
-124.19 4
< 0.1%
-124.18 6
< 0.1%
ValueCountFrequency (%)
-114.31 1
 
< 0.1%
-114.47 1
 
< 0.1%
-114.49 1
 
< 0.1%
-114.55 1
 
< 0.1%
-114.56 1
 
< 0.1%
-114.57 3
< 0.1%
-114.58 2
< 0.1%
-114.59 1
 
< 0.1%
-114.6 3
< 0.1%
-114.61 3
< 0.1%

latitude
Real number (ℝ)

Distinct861
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.633221
Minimum32.54
Maximum41.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size835.3 KiB
2023-02-02T18:25:09.558289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum32.54
5-th percentile32.82
Q133.93
median34.26
Q337.72
95-th percentile38.96
Maximum41.95
Range9.41
Interquartile range (IQR)3.79

Descriptive statistics

Standard deviation2.1363477
Coefficient of variation (CV)0.059953818
Kurtosis-1.1195226
Mean35.633221
Median Absolute Deviation (MAD)1.23
Skewness0.46493428
Sum728093.61
Variance4.5639814
MonotonicityNot monotonic
2023-02-02T18:25:09.678008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.06 241
 
1.2%
34.08 232
 
1.1%
34.05 229
 
1.1%
34.07 227
 
1.1%
34.04 215
 
1.1%
34.09 209
 
1.0%
34.02 207
 
1.0%
34.1 201
 
1.0%
34.03 189
 
0.9%
33.93 181
 
0.9%
Other values (851) 18302
89.6%
ValueCountFrequency (%)
32.54 1
 
< 0.1%
32.55 3
 
< 0.1%
32.56 10
 
< 0.1%
32.57 18
0.1%
32.58 26
0.1%
32.59 11
0.1%
32.6 9
 
< 0.1%
32.61 14
0.1%
32.62 13
0.1%
32.63 18
0.1%
ValueCountFrequency (%)
41.95 2
< 0.1%
41.92 1
 
< 0.1%
41.88 1
 
< 0.1%
41.86 3
< 0.1%
41.84 1
 
< 0.1%
41.82 1
 
< 0.1%
41.81 2
< 0.1%
41.8 3
< 0.1%
41.79 1
 
< 0.1%
41.78 3
< 0.1%

housing_median_age
Real number (ℝ)

Distinct52
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.633094
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size835.3 KiB
2023-02-02T18:25:09.797500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q118
median29
Q337
95-th percentile52
Maximum52
Range51
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.591805
Coefficient of variation (CV)0.43976405
Kurtosis-0.80101334
Mean28.633094
Median Absolute Deviation (MAD)10
Skewness0.061605426
Sum585060
Variance158.55356
MonotonicityNot monotonic
2023-02-02T18:25:09.922615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 1265
 
6.2%
36 856
 
4.2%
35 818
 
4.0%
16 762
 
3.7%
17 694
 
3.4%
34 682
 
3.3%
26 611
 
3.0%
33 609
 
3.0%
25 562
 
2.8%
32 560
 
2.7%
Other values (42) 13014
63.7%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 58
 
0.3%
3 62
 
0.3%
4 190
0.9%
5 242
1.2%
6 157
0.8%
7 173
0.8%
8 203
1.0%
9 204
1.0%
10 263
1.3%
ValueCountFrequency (%)
52 1265
6.2%
51 47
 
0.2%
50 135
 
0.7%
49 133
 
0.7%
48 174
 
0.9%
47 195
 
1.0%
46 245
 
1.2%
45 286
 
1.4%
44 353
 
1.7%
43 351
 
1.7%

total_rooms
Real number (ℝ)

Distinct5911
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2636.5042
Minimum2
Maximum39320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size835.3 KiB
2023-02-02T18:25:10.048494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile622
Q11450
median2127
Q33143
95-th percentile6217
Maximum39320
Range39318
Interquartile range (IQR)1693

Descriptive statistics

Standard deviation2185.2696
Coefficient of variation (CV)0.82885115
Kurtosis32.713859
Mean2636.5042
Median Absolute Deviation (MAD)795
Skewness4.1588164
Sum53871691
Variance4775403.1
MonotonicityNot monotonic
2023-02-02T18:25:10.172178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1527 18
 
0.1%
1582 17
 
0.1%
1613 17
 
0.1%
2127 16
 
0.1%
1471 15
 
0.1%
2053 15
 
0.1%
1607 15
 
0.1%
1722 15
 
0.1%
1717 15
 
0.1%
1703 15
 
0.1%
Other values (5901) 20275
99.2%
ValueCountFrequency (%)
2 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
15 2
< 0.1%
16 1
 
< 0.1%
18 4
< 0.1%
19 2
< 0.1%
20 2
< 0.1%
ValueCountFrequency (%)
39320 1
< 0.1%
37937 1
< 0.1%
32627 1
< 0.1%
32054 1
< 0.1%
30450 1
< 0.1%
30405 1
< 0.1%
30401 1
< 0.1%
28258 1
< 0.1%
27870 1
< 0.1%
27700 1
< 0.1%

total_bedrooms
Real number (ℝ)

Distinct1923
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean537.87055
Minimum1
Maximum6445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size835.3 KiB
2023-02-02T18:25:10.303782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile137
Q1296
median435
Q3647
95-th percentile1275.4
Maximum6445
Range6444
Interquartile range (IQR)351

Descriptive statistics

Standard deviation421.38507
Coefficient of variation (CV)0.78343213
Kurtosis21.985575
Mean537.87055
Median Absolute Deviation (MAD)162
Skewness3.4595463
Sum10990309
Variance177565.38
MonotonicityNot monotonic
2023-02-02T18:25:10.419215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
280 55
 
0.3%
331 51
 
0.2%
345 50
 
0.2%
343 49
 
0.2%
393 49
 
0.2%
328 48
 
0.2%
348 48
 
0.2%
394 48
 
0.2%
272 47
 
0.2%
309 47
 
0.2%
Other values (1913) 19941
97.6%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 2
 
< 0.1%
3 5
< 0.1%
4 7
< 0.1%
5 6
< 0.1%
6 5
< 0.1%
7 6
< 0.1%
8 8
< 0.1%
9 7
< 0.1%
10 8
< 0.1%
ValueCountFrequency (%)
6445 1
< 0.1%
6210 1
< 0.1%
5471 1
< 0.1%
5419 1
< 0.1%
5290 1
< 0.1%
5033 1
< 0.1%
5027 1
< 0.1%
4957 1
< 0.1%
4952 1
< 0.1%
4819 1
< 0.1%

population
Real number (ℝ)

Distinct3879
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1424.9469
Minimum3
Maximum35682
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size835.3 KiB
2023-02-02T18:25:10.544115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile348
Q1787
median1166
Q31722
95-th percentile3284.4
Maximum35682
Range35679
Interquartile range (IQR)935

Descriptive statistics

Standard deviation1133.2085
Coefficient of variation (CV)0.79526363
Kurtosis74.060888
Mean1424.9469
Median Absolute Deviation (MAD)439
Skewness4.9600165
Sum29115941
Variance1284161.5
MonotonicityNot monotonic
2023-02-02T18:25:10.662010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
891 25
 
0.1%
1052 24
 
0.1%
850 24
 
0.1%
1227 24
 
0.1%
761 24
 
0.1%
825 22
 
0.1%
782 22
 
0.1%
1005 22
 
0.1%
872 21
 
0.1%
753 21
 
0.1%
Other values (3869) 20204
98.9%
ValueCountFrequency (%)
3 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
8 4
< 0.1%
9 2
< 0.1%
11 1
 
< 0.1%
13 4
< 0.1%
14 3
< 0.1%
15 2
< 0.1%
17 2
< 0.1%
ValueCountFrequency (%)
35682 1
< 0.1%
28566 1
< 0.1%
16305 1
< 0.1%
16122 1
< 0.1%
15507 1
< 0.1%
15037 1
< 0.1%
13251 1
< 0.1%
12873 1
< 0.1%
12427 1
< 0.1%
12203 1
< 0.1%

households
Real number (ℝ)

Distinct1809
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499.43347
Minimum1
Maximum6082
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size835.3 KiB
2023-02-02T18:25:10.789394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile125
Q1280
median409
Q3604
95-th percentile1159
Maximum6082
Range6081
Interquartile range (IQR)324

Descriptive statistics

Standard deviation382.29923
Coefficient of variation (CV)0.76546578
Kurtosis22.094083
Mean499.43347
Median Absolute Deviation (MAD)151
Skewness3.4138502
Sum10204924
Variance146152.7
MonotonicityNot monotonic
2023-02-02T18:25:10.906601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
306 57
 
0.3%
335 56
 
0.3%
282 55
 
0.3%
386 55
 
0.3%
429 54
 
0.3%
284 51
 
0.2%
375 51
 
0.2%
297 51
 
0.2%
278 50
 
0.2%
380 50
 
0.2%
Other values (1799) 19903
97.4%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 3
 
< 0.1%
3 4
 
< 0.1%
4 4
 
< 0.1%
5 7
< 0.1%
6 5
< 0.1%
7 10
< 0.1%
8 8
< 0.1%
9 9
< 0.1%
10 7
< 0.1%
ValueCountFrequency (%)
6082 1
< 0.1%
5358 1
< 0.1%
5189 1
< 0.1%
5050 1
< 0.1%
4930 1
< 0.1%
4855 1
< 0.1%
4769 1
< 0.1%
4616 1
< 0.1%
4490 1
< 0.1%
4372 1
< 0.1%

median_income
Real number (ℝ)

Distinct12825
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8711616
Minimum0.4999
Maximum15.0001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size835.3 KiB
2023-02-02T18:25:11.034303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.4999
5-th percentile1.60066
Q12.5637
median3.5365
Q34.744
95-th percentile7.30034
Maximum15.0001
Range14.5002
Interquartile range (IQR)2.1803

Descriptive statistics

Standard deviation1.8992912
Coefficient of variation (CV)0.49062567
Kurtosis4.9431411
Mean3.8711616
Median Absolute Deviation (MAD)1.0649
Skewness1.6445569
Sum79099.445
Variance3.6073072
MonotonicityNot monotonic
2023-02-02T18:25:11.156188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.125 49
 
0.2%
15.0001 48
 
0.2%
2.875 46
 
0.2%
4.125 44
 
0.2%
2.625 44
 
0.2%
3.875 41
 
0.2%
3.375 38
 
0.2%
4 37
 
0.2%
3 37
 
0.2%
3.625 36
 
0.2%
Other values (12815) 20013
97.9%
ValueCountFrequency (%)
0.4999 12
0.1%
0.536 10
< 0.1%
0.5495 1
 
< 0.1%
0.6433 1
 
< 0.1%
0.6775 1
 
< 0.1%
0.6825 1
 
< 0.1%
0.6831 1
 
< 0.1%
0.696 1
 
< 0.1%
0.6991 1
 
< 0.1%
0.7007 1
 
< 0.1%
ValueCountFrequency (%)
15.0001 48
0.2%
15 2
 
< 0.1%
14.9009 1
 
< 0.1%
14.5833 1
 
< 0.1%
14.4219 1
 
< 0.1%
14.4113 1
 
< 0.1%
14.2959 1
 
< 0.1%
14.2867 1
 
< 0.1%
13.947 1
 
< 0.1%
13.8556 1
 
< 0.1%

target
Real number (ℝ)

Distinct3833
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean206864.41
Minimum14999
Maximum500001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size835.3 KiB
2023-02-02T18:25:11.282926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum14999
5-th percentile66260
Q1119500
median179700
Q3264700
95-th percentile490560
Maximum500001
Range485002
Interquartile range (IQR)145200

Descriptive statistics

Standard deviation115435.67
Coefficient of variation (CV)0.55802574
Kurtosis0.32803747
Mean206864.41
Median Absolute Deviation (MAD)68400
Skewness0.97828989
Sum4.2268606 × 109
Variance1.3325393 × 1010
MonotonicityNot monotonic
2023-02-02T18:25:11.407604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500001 958
 
4.7%
137500 119
 
0.6%
162500 116
 
0.6%
112500 103
 
0.5%
187500 92
 
0.5%
225000 91
 
0.4%
350000 79
 
0.4%
87500 77
 
0.4%
275000 65
 
0.3%
150000 64
 
0.3%
Other values (3823) 18669
91.4%
ValueCountFrequency (%)
14999 4
< 0.1%
17500 1
 
< 0.1%
22500 4
< 0.1%
25000 1
 
< 0.1%
26600 1
 
< 0.1%
26900 1
 
< 0.1%
27500 1
 
< 0.1%
28300 1
 
< 0.1%
30000 2
< 0.1%
32500 4
< 0.1%
ValueCountFrequency (%)
500001 958
4.7%
500000 27
 
0.1%
499100 1
 
< 0.1%
499000 1
 
< 0.1%
498800 1
 
< 0.1%
498700 1
 
< 0.1%
498600 1
 
< 0.1%
498400 1
 
< 0.1%
497600 1
 
< 0.1%
497400 1
 
< 0.1%

ocean_proximity
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size835.3 KiB
<1H OCEAN
9034 
INLAND
6496 
NEAR OCEAN
2628 
NEAR BAY
2270 
ISLAND
 
5

Length

Max length10
Median length9
Mean length8.0630353
Min length6

Characters and Unicode

Total characters164752
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNEAR BAY
2nd rowNEAR BAY
3rd rowNEAR BAY
4th rowNEAR BAY
5th rowNEAR BAY

Common Values

ValueCountFrequency (%)
<1H OCEAN 9034
44.2%
INLAND 6496
31.8%
NEAR OCEAN 2628
 
12.9%
NEAR BAY 2270
 
11.1%
ISLAND 5
 
< 0.1%

Length

2023-02-02T18:25:11.522609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T18:25:11.649329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
ocean 11662
33.9%
1h 9034
26.3%
inland 6496
18.9%
near 4898
14.3%
bay 2270
 
6.6%
island 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 29557
17.9%
A 25331
15.4%
E 16560
10.1%
13932
8.5%
O 11662
 
7.1%
C 11662
 
7.1%
< 9034
 
5.5%
1 9034
 
5.5%
H 9034
 
5.5%
I 6501
 
3.9%
Other values (6) 22445
13.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 132752
80.6%
Space Separator 13932
 
8.5%
Math Symbol 9034
 
5.5%
Decimal Number 9034
 
5.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 29557
22.3%
A 25331
19.1%
E 16560
12.5%
O 11662
 
8.8%
C 11662
 
8.8%
H 9034
 
6.8%
I 6501
 
4.9%
L 6501
 
4.9%
D 6501
 
4.9%
R 4898
 
3.7%
Other values (3) 4545
 
3.4%
Space Separator
ValueCountFrequency (%)
13932
100.0%
Math Symbol
ValueCountFrequency (%)
< 9034
100.0%
Decimal Number
ValueCountFrequency (%)
1 9034
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 132752
80.6%
Common 32000
 
19.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 29557
22.3%
A 25331
19.1%
E 16560
12.5%
O 11662
 
8.8%
C 11662
 
8.8%
H 9034
 
6.8%
I 6501
 
4.9%
L 6501
 
4.9%
D 6501
 
4.9%
R 4898
 
3.7%
Other values (3) 4545
 
3.4%
Common
ValueCountFrequency (%)
13932
43.5%
< 9034
28.2%
1 9034
28.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 164752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 29557
17.9%
A 25331
15.4%
E 16560
10.1%
13932
8.5%
O 11662
 
7.1%
C 11662
 
7.1%
< 9034
 
5.5%
1 9034
 
5.5%
H 9034
 
5.5%
I 6501
 
3.9%
Other values (6) 22445
13.6%

Interactions

2023-02-02T18:25:07.874834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:00.018084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:00.953848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:01.839426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:02.789159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:03.824060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:04.933619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:05.937141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:06.879483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:07.969186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:00.121380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:01.047274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:01.941142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:02.896070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:03.922830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:05.035291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:06.035236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:06.982204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:08.062845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:00.213287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:01.136574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:02.036838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:03.004108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:04.023429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:05.132765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:06.129043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:07.083043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:08.164818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:00.319193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:01.229366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:02.133181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:03.165133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:04.120466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:05.231262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:06.229919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:07.190603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:08.277423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:00.428573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:01.336624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:02.242215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:03.275666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:04.241831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:05.344831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:06.340059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:07.306875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:08.383656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:00.530683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:01.433025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:02.344980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:03.385127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:04.435658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:05.448249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:06.444256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:07.424619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:08.505630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:00.641136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:01.535222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:02.461530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:03.497736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:04.554997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:05.572846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:06.552713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:07.528703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:08.622090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:00.745632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:01.645118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:02.571628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:03.608241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:04.661451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:05.717336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:06.660238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:07.648527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:08.727229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:00.853340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:01.744080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:02.687481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:03.719919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:04.816278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:05.832941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:06.774822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-02T18:25:07.765594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-02T18:25:11.744811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incometargetocean_proximity
longitude1.000-0.879-0.1520.0410.0640.1240.061-0.010-0.0700.425
latitude-0.8791.0000.033-0.019-0.057-0.124-0.075-0.088-0.1660.470
housing_median_age-0.1520.0331.000-0.356-0.307-0.283-0.281-0.1460.0750.191
total_rooms0.041-0.019-0.3561.0000.9150.8160.9060.2710.2050.021
total_bedrooms0.064-0.057-0.3070.9151.0000.8710.976-0.0060.0860.017
population0.124-0.124-0.2830.8160.8711.0000.9040.0050.0030.014
households0.061-0.075-0.2810.9060.9760.9041.0000.0290.1120.019
median_income-0.010-0.088-0.1460.271-0.0060.0050.0291.0000.6770.125
target-0.070-0.1660.0750.2050.0860.0030.1120.6771.0000.302
ocean_proximity0.4250.4700.1910.0210.0170.0140.0190.1250.3021.000

Missing values

2023-02-02T18:25:08.861528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-02T18:25:09.031468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incometargetocean_proximity
0-122.2337.8841.0880.0129.0322.0126.08.3252452600.0NEAR BAY
1-122.2237.8621.07099.01106.02401.01138.08.3014358500.0NEAR BAY
2-122.2437.8552.01467.0190.0496.0177.07.2574352100.0NEAR BAY
3-122.2537.8552.01274.0235.0558.0219.05.6431341300.0NEAR BAY
4-122.2537.8552.01627.0280.0565.0259.03.8462342200.0NEAR BAY
5-122.2537.8552.0919.0213.0413.0193.04.0368269700.0NEAR BAY
6-122.2537.8452.02535.0489.01094.0514.03.6591299200.0NEAR BAY
7-122.2537.8452.03104.0687.01157.0647.03.1200241400.0NEAR BAY
8-122.2637.8442.02555.0665.01206.0595.02.0804226700.0NEAR BAY
9-122.2537.8452.03549.0707.01551.0714.03.6912261100.0NEAR BAY
longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incometargetocean_proximity
20630-121.3239.2911.02640.0505.01257.0445.03.5673112000.0INLAND
20631-121.4039.3315.02655.0493.01200.0432.03.5179107200.0INLAND
20632-121.4539.2615.02319.0416.01047.0385.03.1250115600.0INLAND
20633-121.5339.1927.02080.0412.01082.0382.02.549598300.0INLAND
20634-121.5639.2728.02332.0395.01041.0344.03.7125116800.0INLAND
20635-121.0939.4825.01665.0374.0845.0330.01.560378100.0INLAND
20636-121.2139.4918.0697.0150.0356.0114.02.556877100.0INLAND
20637-121.2239.4317.02254.0485.01007.0433.01.700092300.0INLAND
20638-121.3239.4318.01860.0409.0741.0349.01.867284700.0INLAND
20639-121.2439.3716.02785.0616.01387.0530.02.388689400.0INLAND